Use LangChain’s agent API when a conventional tool-using agent fits the job and you want a higher-level starting point. Build directly with LangGraph when you need to define the workflow’s steps, shared state, routing, or pause-and-resume behavior yourself. These are related approaches: LangChain agent implementations use LangGraph primitives, so the choice is mainly how much workflow control your application needs.
How the two approaches relate
LangChain agents provide a higher-level way to build common agent patterns. The LangChain learning guide describes them as an easy place to start and includes examples such as RAG and SQL agents. Their implementations use LangGraph primitives; using LangChain’s agent API does not mean choosing a separate underlying foundation. Direct LangGraph construction gives you a lower-level path for customizing how the application runs.
That distinction makes this an architectural choice, not a contest over which framework is universally better. Start with the abstraction that covers the application’s needs, then move toward an explicit graph if the built-in agent behavior does not give you enough control. See the LangChain learning guide for its agent and workflow learning paths.
Choose based on the workflow you need to own
| Decision area | LangChain agent API | Direct LangGraph construction |
|---|---|---|
| Initial implementation | A good starting point when a conventional tool-using agent covers the task. | You model the workflow in nodes, state, and transitions. |
| Control flow | Fits while the built-in agent behavior is sufficient. | Fits when you need explicit steps, branching, retries, or workflow-specific routing. |
| State and inspection | Can keep a simple agent implementation concise. | Shared state and node boundaries expose intermediate work and control flow, which can help with debugging and recovery. |
| Pause and resume | Possible through underlying LangGraph primitives when configured. | You can design interruptions and checkpointed resume behavior directly into the graph. |
| Learning path | Begin with agent tutorials, including RAG and SQL examples. | Use custom workflow tutorials when the agent abstraction does not provide enough control. |
When a LangChain agent is a good fit
Choose the LangChain agent API when your application is a relatively direct tool-using agent and you do not need to control every workflow transition. It is a sensible first implementation if the standard agent pattern covers the task, and the learning guide provides examples for common applications such as RAG and SQL.
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It is also a useful way to avoid designing a graph before you know you need one. If later requirements call for explicit stages, custom routing, or checkpoints, the shared LangGraph foundation gives you a path toward more control.
When to build directly with LangGraph
Use direct LangGraph construction when the application behaves more like a defined process than a single agent loop: it has distinct stages, conditional transitions, or data that must be shared across steps. The Thinking in LangGraph guide recommends identifying the workflow, breaking it into steps, designing shared state, and then connecting nodes and routing decisions.
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Model work as nodes and state
A LangGraph node is a function that receives the current state and returns updates. The graph connects these nodes through transitions and routing. This structure makes the sequence and branching of work explicit, rather than leaving those decisions inside a higher-level agent abstraction.
Account for node boundaries
Node size affects how work is divided and inspected. Smaller or more numerous nodes can create more checkpoint boundaries and make intermediate work easier to examine. If a failure occurs, however, work within the node where execution stopped may need to be repeated. The guide says checkpoints are written asynchronously by default and that adding nodes does not necessarily make execution slower; actual performance and durability depend on your storage and configuration.
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Designing pause, review, and resume
A graph can support human review by compiling with a checkpointer, invoking the workflow with a thread ID, and pausing at an interrupt. The saved state can then be resumed with human input. This is useful when a task needs approval or a person must supply information before the remaining steps continue. The same underlying capabilities are available to LangChain agent implementations when configured, so human review alone does not require abandoning the agent API; choose direct graph construction when you need to shape that workflow explicitly.
A practical decision path
- Describe the work. If it is a conventional agent using tools and its behavior fits the built-in pattern, start with the LangChain agent API.
- Identify control requirements. If you must define workflow-specific stages, branches, retries, or routing, build the graph directly.
- Map state across steps. If intermediate results need to be explicit and shared across stages, model them in graph state.
- Check review and recovery needs. If work must pause for a person and resume later, determine whether configured agent behavior is sufficient or whether you need explicit graph-level interruptions and checkpoints.
- Keep the choice reversible where possible. Begin with the smallest abstraction that meets requirements; use the shared LangGraph foundation as a route to more explicit control if those requirements grow.
The official learning guide also presents agent patterns, custom workflows, and multi-agent tutorials that combine them. The approaches are therefore not mutually exclusive architectural camps; the application’s required control and state should determine the level you use.
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Do not confuse either approach with Deep Agents
Deep Agents is a separate harness built on LangChain building blocks and LangGraph tooling. Its documentation lists capabilities such as planning, filesystem-based context management, subagents, long-term memory, and human approval for complex tasks. Those are Deep Agents capabilities; they should not be assumed to be part of the basic LangChain agent API or prerequisites for a LangGraph application. See the Deep Agents overview.
Check the documentation for your language and version
The comparison here describes the documented architectural relationship and workflow patterns, not a version-by-version feature matrix. The cited learning guide is for Python, while the workflow and Deep Agents pages linked above are JavaScript documentation. The cited material does not establish current Python and JavaScript package compatibility, exact API parity, or migration steps. Confirm the live API reference and release notes for the language and package versions you intend to use; these APIs and documentation can change.
Quick Recap
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